The present invention relates generally to diagnostics of railroad locomotives and other self-powered transportation equipment, and, more specifically, to system and method for processing a new diagnostics case relative to historical case data and determine a ranking for possible repairs. This ranking may be used for condensing knowledge gained from screening cases similar to the new case for the type of machine undergoing diagnostics.
A machine, such as a locomotive or other complex systems used in industrial processes, medical imaging, telecommunications, aerospace applications, power generation, etc., includes elaborate controls and sensors that generate faults when anomalous operating conditions of the machine are encountered. Typically, a field engineer will look at a fault log and determine whether a repair is necessary.
Approaches like neural networks, decision trees, etc., have been employed to learn over input data to provide prediction, classification, and function approximation capabilities in the context of diagnostics. Often, such approaches have required structured and relatively static and complete input data sets for learning, and have produced models that resist real-world interpretation.
Another approach, Case Based Reasoning (CBR), is based on the observation that experiential knowledge (memory of past experiences or cases) is applicable to problem solving as learning rules or behaviors. CBR relies on relatively few pre-processing of raw knowledge, focusing instead on indexing, retrieval, reuse, and archival of cases. In the diagnostic context, a case generally refers to a problem/solution description pair that represents a diagnosis of a problem and an appropriate repair. CBR assumes cases described by a fixed, known number of descriptive attributes. Conventional CBR systems assume a corpus of fully valid or “gold standard” cases that new incoming cases can be matched against.
U.S. Pat. No. 5,463,768 discloses an approach that uses error log data and assumes predefined cases with each case associating an input error log to a verified, unique diagnosis of a problem. In particular, a plurality of historical error logs is grouped into case sets of common malfunctions. From the group of case sets, common patterns, i.e., consecutive rows or strings of data, are labeled as a block. Blocks are used to characterize fault contribution for new error logs that are received in a diagnostic unit. Unfortunately, for a continuous fault code stream where any or all possible fault codes may occur from zero to any finite number of times and where the fault codes may occur in any order, predefining the structure of a case is nearly impossible.
U.S. Pat. No. 6,343,236, assigned in common to the same assignee of the present invention, discloses system and method for processing historical repair data and fault log data, which is not restricted to sequential occurrences of fault log entries and which provides weighted repair and distinct fault cluster combinations, to facilitate analysis of new fault log data from a malfunctioning machine. Further, U.S. Pat. No. 6,415,395, also assigned to the same assignee of the present invention, discloses system and method for analyzing new fault log data from a malfunctioning machine in which the system and method are not restricted to sequential occurrences of fault log entries, and wherein the system and method predict one or more repair actions using predetermined weighted repair and distinct fault cluster combinations.
Additionally, U.S. Pat. No. 6,336,065, assigned to the same assignee of the present invention, provides system and method that uses snapshot observations of operational parameters from the machine in combination with the fault log data in order to further enhance the predictive accuracy of the diagnostic algorithms used therein. That invention further provides noise reduction filters, to substantially eliminate undesirable noise, e.g., unreliable or useless information that may be present in the fault log data and/or the operational parameter data.
It is believed that the inventive concepts disclosed in the foregoing patents provide substantial advantages and advancements in the art of computerized diagnostics. However, the case-based reasoning tools described in the foregoing patents generally rely on associating probabilistic outcomes with individual features of a given case. The calculation of these probabilistic outcomes may be somewhat time consuming, as may be identification of relevant features within a new case. In addition, not all features have the same amount of reliability and history associated with them, and calculating their probabilistic relevance based on limited data could lead to inaccurate outcomes.